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- 저자 : 전민진(Min Jin Jeon) (검색결과 3 건)
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전민진(Min Jin Jeon),황지원(Ji Won Hwang),김종우(Jong Woo Kim) 한국지능정보시스템학회 2021 지능정보연구 Vol.27 No.4
Aspect Based Sentiment Analysis (ABSA), which analyzes sentiment based on aspects that appear in the text, is drawing attention because it can be used in various business industries. ABSA is a study that analyzes sentiment by aspects for multiple aspects that a text has. It is being studied in various forms depending on the purpose, such as analyzing all targets or just aspects and sentiments. Here, the aspect refers to the property of a target, and the target refers to the text that causes the sentiment. For example, for restaurant reviews, you could set the aspect into food taste, food price, quality of service, mood of the restaurant, etc. Also, if there is a review that says, The pasta was delicious, but the salad was not, the words steak and salad, which are directly mentioned in the sentence, become the “target.” So far, in ABSA, most studies have analyzed sentiment only based on aspects or targets. However, even with the same aspects or targets, sentiment analysis may be inaccurate. Instances would be when aspects or sentiment are divided or when sentiment exists without a target. For example, sentences like, Pizza and the salad were good, but the steak was disappointing. Although the aspect of this sentence is limited to “food,” conflicting sentiments coexist. In addition, in the case of sentences such as Shrimp was delicious, but the price was extravagant, although the target here is “shrimp,” there are opposite sentiments coexisting that are dependent on the aspect. Finally, in sentences like The food arrived too late and is cold now. there is no target (NULL), but it transmits a negative sentiment toward the aspect service. Like this, failure to consider both aspects and targets – when sentiment or aspect is divided or when sentiment exists without a target – creates a dual dependency problem. To address this problem, this research analyzes sentiment by considering both aspects and targets (Target-Aspect-Sentiment Detection, hereby TASD). This study detected the limitations of existing research in the field of TASD: local contexts are not fully captured, and the number of epochs and batch size dramatically lowers the F1-score. The current model excels in spotting overall context and relations between each word. However, it struggles with phrases in the local context and is relatively slow when learning. Therefore, this study tries to improve the models performance. To achieve the objective of this research, we additionally used auxiliary loss in aspect-sentiment classification by constructing CNN(Convolutional Neural Network) layers parallel to existing models. If existing models have analyzed aspect-sentiment through BERT encoding, Pooler, and Linear layers, this research added CNN layer-adaptive average pooling to existing models, and learning was progressed by adding additional loss values for aspect-sentiment to existing loss. In other words, when learning, the auxiliary loss, computed through CNN layers, allowed the local context to be captured more fitted. After learning, the model is designed to do aspect-sentiment analysis through the existing method. To evaluate the performance of this model, two datasets, SemEval-2015 task 12 and SemEval-2016 task 5, were used and the f1-score increased compared to the existing models. When the batch was 8 and epoch was 5, the difference was largest between the F1-score of existing models and this study with 29 and 45, respectively. Even when batch and epoch were adjusted, the F1-scores were higher than the existing models. It can be said that even when the batch and epoch numbers were small, they can be learned effectively compared to the existing models. Therefore, it can be useful in situations where resources are limited. Through this study, aspect-based sentiments can be more accurately analyzed. Through various uses in business, such as development or establishing marketing strategies, both consumers and sellers will be able to
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김지혜 ( Ji Hye Kim ),최윤정 ( Yun Jung Choi ),김민정 ( Min Jung Kim ),박지수 ( Ji Soo Park ),전민진 ( Min Jin Jeon ),서동인 ( Dong In Suh ) 대한천식알레르기학회 2021 Allergy Asthma & Respiratory Disease Vol.9 No.4
Purpose: Sleep-disordered breathing is one of the complicating characteristics in patients with Prader-Willi syndrome (PWS). No de-tailed description and risk factors are suggested on breathing problems during sleep in Korean children with PWS. Methods: We reviewed clinical and sleep-study data in patients with PWS who underwent polysomnography before they took the growth hormone therapy. Results: Of the 27 patients with PWS, 25 (92.6%) had sleep-disordered breathing, of whom 14 showed moderate to severe sleep ap-nea. Obstructive dominance was prevalent (64%), followed by central dominance (24%). The apnea-hypopnea index (AHI) increased with increasing weight-for-height z-score (WHZ) (r=0.50, P=0.009), but did not differ by age. Apnea duration of over 12 months was longer in the patient group than in the infant group (15.1±4.3 seconds vs. 9.4±1.7 seconds, P=0.001) and in the obese than non- obese groups (16.8±4.3 seconds vs. 10.0±2.0 seconds, P=0.003). Desaturation below 70% was more common in the obese than nonobese subjects (3/9 vs. 0/18, P=0.029). Age was not different between the central and obstructive apnea groups, but patients with central apnea tended to be younger than patients with obstructive apnea (median [range]: 8.0 months [6.0-12.0 months] vs. 16.5 months [8.5-79.5 months], P=0.092). In addition, patients with obstructive apnea showed higher AHI (12.8 [5.9-19.2] vs. 3.9 [3.4-4.5], P=0.045). Conclusion: Sleep-disordered breathing is common in PWS children with different intensity and patterns according to age and BMI. Close monitoring of breathing problems during sleep is required in PWS patients. (Allergy Asthma Respir Dis 2021;9:216-224)
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